Publications by Jimmy Ng
ML Ensembling
Ensembling methods Ensembling is a very common and powerful technique to enhance accuracy and overall performance in dealing with classification problem. Given a binary outcome, we would like to classify whether an event would occur based on a set of quantitative or qualitative variables. There are many algorithms to do the trick. In this blog po...
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data_624_group_3_hw_1
First, we load the requisite packages. HA 2 2.1 Use the help function to explore what the series gold, woolyrnq and gas represent. Each is a dataset contained within the forecast package. The gold dataset contains time series data of daily morning gold prices in US dollars from 1/1/1985 to 3/31/1989. The woolyrnq dataset includes quarterly woo...
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data_624_group_3_project_1
Set up First, we load a list of packages (such as tidyverse, fpp2) that are necessary for this session. We read the raw data from excel and transform the SeriesInd into date format. Our goal is to forecast 140 periods for each group and for different subset of variables. Second, we split the data into six groups by the group variable. We check th...
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data_624_group_3_project_2
# detect, use multicores numCores <- parallel::detectCores() # create a simple cluster on the local machine using all available threads cl <- parallel::makeCluster(detectCores(), methods = FALSE) # register our cluster doParallel::registerDoParallel(cl) MARS # model set.seed(1234) marsModel <- caret::train(x = bev_model_train %>% dplyr:...
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Penguins: log reg, multinom log reg classification
load packages, data if(!require(pacman)){install.packages("pacman"); require(pacman)} ## Loading required package: pacman ## Warning: package 'pacman' was built under R version 3.6.2 packages <- c('nnet', 'glue', 'broom', 'MASS', 'caret', 'InformationValue', 'Hmisc', 'kableExtra', 'corrplot', 'tidyverse', 'ROCR', 'palmerpenguins', 'mice') pac...
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data 622 hw2: LDA, QDA, NB models
load packages, data This was the second assignment from the class data 622. We would use the “penguin” data set and apply three distinct classification algorithms to predict for “species”. if(!require(pacman)){install.packages("pacman"); require(pacman)} ## Loading required package: pacman ## Warning: package 'pacman' was built under R ...
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k-means clustering, PCA, and SVM on mental health questionnaire
Code Show All Code Hide All Code Group 4 Assignment 4 Group 4 Assignment 4 Data Exploration Question 1 K-Means Clustering Data preparation for Q1 Question 2 Principal Component Analysis (PCA) data prep for Q2 PCA - ADHD PCA - MD Question 3 Support Vector Machine (SVM) data prep SVM linear SVM non-linear SVM result (without PCA) SVM with...
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data 605: final
Code Show All Code Hide All Code data_605_final data_605_final Final Problem 1 Final Problem 2 Final Problem 3 Jimmy Ng 12/15/2021 Final Problem 1 You’ll verify for yourself that PageRank works by performing calculations on a small universe of web pages. Let’s use the 6 page universe that we had in the previous discussion For this di...
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data 605: hw 15
Code Show All Code Hide All Code data_605_hw15 data_605_hw15 Question 1 Question 2 Question 3 Question 4 Question 5 Jimmy Ng 12/8/2021 Question 1 Find the equation of the regression line for the given points. Round any final values to the nearest hundredth, if necessary. (5.6,8.8), (6.3,12.4), (7,14.8), (7.7,18.2), (8.4,20.8) x <- c(5.6,...
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data 605: Taylor Series
Code Show All Code Hide All Code data_605_hw14 data_605_hw14 Taylor Series Expansion Jimmy Ng 12/1/2021 This week, we’ll work out some Taylor Series expansions of popular functions. For each function, only consider its valid ranges as indicated in the notes when you are computing the Taylor Series expansion. Taylor Series Expansion p...
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